Risk analysis system and risk analysis method

ABSTRACT

The risk analysis system according to the present invention includes: a storage apparatus which stores subject data including information related to health of a subject; an analyzer which analyzes a risk related to the health of the subject based on the subject data acquired from the storage apparatus; and an output apparatus which outputs an analysis result by the analyzer. The analyzer has: a risk estimating unit which estimates an event onset risk of the subject based on the subject data; and a medical expense predicting unit which predicts future medical expenses, which are medical expenses to be incurred in the future by the subject, based on the event onset risk estimated by the risk estimating unit and the subject data.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a Continuation of International Patent ApplicationNo. PCT/JP2017/023511, filed Jun. 27, 2017, which claims the benefit ofJapanese Patent Application No. 2016-134248, filed Jul. 6, 2016. Theseapplications are incorporated by reference herein in their entirety.

BACKGROUND OF THE INVENTION Technical Field

The present invention relates to a technique for analyzing a riskrelated to the health of a subject.

Background Art

Even for a healthy person, a risk of developing a disease is notnonexistent. In order to maintain a healthy and safe lifestyle, it isimportant to accurately assess one's own health risks and factorsthereof, take appropriate measures including improvement of lifestylehabits and necessary treatment, and furthermore, subscribe to healthinsurance and save for the future. Considering health insurance policiesand savings plans should involve roughly estimating medical expenses tobe incurred in the future (hereinafter, referred to as “future medicalexpenses”) to ensure that one's saving is neither excessive nordeficient. However, predicting future medical expenses can bechallenging to a lay-person. In addition, although average futuremedical expenses may be determined by consulting an expert such as afinancial planner, since average future medical expenses do not takehealth conditions and health risks of an individual into consideration,there is a lack in accuracy.

On the other hand, insurers who provide health insurance also have astrong need for predicting, as accurately as possible, health risks andfuture medical expenses of an insurance subscription applicant. Insurersincorporate such information into underwriting health insurance anddesigning terms and conditions (an insurance plan, a coverage amount,add-on options, and the like). However, as it stands, there is nomechanism capable of taking health conditions and health risks of anindividual into consideration to predict future medical expenses of theindividual with high reliability.

As an example of related prior art, PTL 1 discloses a system whichautomatically evaluates a propriety of underwriting or terms andconditions of life insurance in response to input of age and gender ofan insurance subscription applicant, contents of a life insurance policy(an amount of death protection, a type of insurance, types of add-onoptions, and the like), and results of a physical checkup (bloodpressure, BMI, uric acid level, and the like). PTL 2 discloses a systemwhich collects measured data of blood pressure of an insured personthrough a network and changes, in accordance with the number of days onwhich blood pressure readings were taken during past three months and aproportion of readings in which the blood pressure fell within aprescribed value, a coverage amount (such as a hospitalization benefit)with respect to the insured person. PTL 3 discloses a system whichanalyzes, from information on statements of medical expenses accumulatedin a certain period of time, medical expenses for each injury or diseaseand an increase in such medical expenses when a plurality of injuriesand diseases occur concurrently and which uses a result of the analysisto find an insured person of which medical expenses have increasedsuddenly and to provide health guidance to the insured person. However,none of the literature discloses prediction of future medical expenses.

CITATION LIST Patent Literature

PTL 1: Japanese Patent Application Laid-open No. 2003-31127

PTL 2: Japanese Patent Application Laid-open No. 2016-4430

PTL 3: Japanese Patent Application Laid-open No. 2011-39653

SUMMARY OF INVENTION

The present invention has been made in consideration of thecircumstances described above and an object thereof is to provide atechnique for predicting an amount of medical expenses to be incurred inthe future with high reliability.

A risk analysis system according to the present invention includes: astorage apparatus which stores subject data including informationrelated to health of a subject; an analyzer which analyzes a riskrelated to the health of the subject based on the subject data acquiredfrom the storage apparatus; and an output apparatus which outputs aresult of an analysis by the analyzer, wherein the analyzer includes: arisk estimating unit which estimates an event onset risk of the subjectbased on the subject data; and a medical expense predicting unit whichpredicts future medical expenses to be incurred in the future by thesubject based on the event onset risk estimated by the risk estimatingunit and the subject data.

According to this configuration, an event onset risk of a subject isfirst estimated and future medical expenses of the subject are predictedbased on the event onset risk. Therefore, highly reliable future medicalexpenses which take health conditions and health risks of the subjectinto consideration can be predicted. In this case, an “event” refers toa phenomenon which adversely affects health and is typically a“disease”. The term “event onset” may be replaced with “disease onset”.

The risk estimating unit may estimate, as the event onset risk, an onsetprobability at which the subject develops a disease, and the medicalexpense predicting unit may calculate, based on the subject data,medical expenses needed when the subject develops the disease, and maypredict, based on the medical expenses and the onset probability, futuremedical expenses of the subject.

According to this configuration, future medical expenses can bepredicted with high reliability using a relatively simple algorithm.Specifically, since “medical expenses needed when a disease develops” isnot dependent on an individual, an average amount of medical expensescan be easily obtained from, for example, previous data on statements ofmedical expenses, statistical data compiled by the Ministry of Health,Labour and Welfare, and the like. In addition, by combining the averagemedical expenses with the “onset probability at which a subject developsa disease” which is individual-dependent, future medical expenses whichtakes health conditions and health risks of the subject intoconsideration can be calculated.

The medical expense predicting unit may calculate, as the medicalexpenses needed when the subject develops the disease, hospitalizationmedical expenses required for hospitalization and outpatient medicalexpenses required for outpatient care. For example, a sum of thehospitalization medical expenses and the outpatient medical expenses maybe adopted as the medical expenses of the subject. According to thisconfiguration, since both hospitalization medical expenses andoutpatient medical expenses are calculated, more realistic medicalexpenses can be predicted.

The risk estimating unit may estimate the onset probability for each ofa plurality of diseases, and the medical expense predicting unit maypredict the future medical expenses for each of the plurality ofdiseases and calculate total future medical expenses of the subject byadding up the predicted future medical expenses. Diseases that a subjectmay potentially develop are not limited to just one disease. Forexample, it is possible that a same subject may develop a plurality ofdiseases such as a cerebrovascular disease and a coronary arterydisease. Therefore, according to this configuration, by estimating anonset probability for each of a plurality of diseases, more realisticmedical expenses can be predicted.

An insurance subscription propriety determining unit may be providedwhich, by estimating an insurance premium amount to be paid when thesubject subscribes to health insurance and comparing the predictedfuture medical expenses with the estimated insurance premium amount,determines a propriety of subscription to the health insurance by thesubject. In addition, the output apparatus may output conditions and/oran insurance premium amount of health insurance suitable for the subjectbased on a determination result from the insurance subscriptionpropriety determining unit. According to this configuration, a proprietyof subscription to health insurance can be readily determined with highreliability. The result of the propriety determination may be used asreference information when a subject considers subscription to healthinsurance or may be used as reference information when an insurer whoprovides health insurance evaluates underwriting or designs terms andconditions for a subject (an insurance subscription applicant).

The output apparatus may generate and output a graph indicating anannual trend of the future medical expenses of the subject. Since anevent onset risk changes over time (normally, the risk increases withthe passage of time), future medical expenses also change over time.Outputting the graph described above enables a change in future medicalexpenses over time to be readily confirmed. For example, the graph maybe used by a subject as reference for a financial plan when consideringsubscription to health insurance or formulating a savings plan or may beused as reference information when an insurer who provides healthinsurance evaluates underwriting or designs terms and conditions for asubject (an insurance subscription applicant).

The analyzer may include a plurality of types of analysis algorithms fordifferent races and/or different areas of residence, and may select ananalysis algorithm in accordance with a race and/or area of residence ofthe subject. Different races carry different event onset risks due todifferences in genetics. In addition, even within the same race, adifference in areas of residence results in a difference in event onsetrisks due to differences in lifestyle habits. Furthermore, differentareas of residence have different healthcare systems and commodityprices, which result in different medical expenses. Therefore, byadopting a configuration in which a suitable analysis algorithm isselected in accordance with the race and/or the area of residence asdescribed above, an event onset risk can be estimated and future medicalexpenses can be predicted with high accuracy.

The analyzer may further include a hypertension determining unit whichdetermines hypertension of the subject based on the subject data, andthe risk estimating unit may estimate the event onset risk of thesubject using a determination result from the hypertension determiningunit as a risk factor. Hypertension is known to elevate an onset risk ofvascular events. Therefore, by considering hypertension as a risk factorwhen estimating an event onset risk, reliability of risk estimation canbe increased.

When it is determined by the hypertension determining unit that thesubject has hypertension, the medical expense predicting unit maypredict the future medical expenses of the subject by also takingmedical expenses needed to treat hypertension into consideration. Forexample, hypertension often requires continuous treatment includinghospital visits and medication. Therefore, by also taking medicalexpenses needed to treat hypertension into consideration, accuracy ofprediction of future medical expenses can be further increased.

The present invention can be considered a risk analysis system includingat least a part of the components or functions described above. Inaddition, the present invention can also be considered a risk analysismethod which includes at least a part of the processes described above.Furthermore, the present invention can also be considered a program forexecuting each step of a risk analysis method which includes at least apart of the processes described above or a computer-readable recordingmedium which records such a program on a non-transitory basis. Moreover,the present invention can also be considered a display apparatus or aterminal which displays an analysis result output by the risk analysissystem or the risk analysis method described above. The respectivecomponents and processes described above can be combined with oneanother in any way possible to constitute the present invention insofaras technical contradictions do not arise.

According to the present invention, an amount of medical expenses to beincurred in the future can be predicted with high reliability.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a diagram showing an overall configuration of a risk analysissystem according to an embodiment of the present invention;

FIG. 2 is a block diagram showing a functional configuration of ananalyzer;

FIG. 3 is a flow chart showing a specific flow of a prediction processof future medical expenses;

FIG. 4 is a flow chart showing a specific flow of an insurancesubscription propriety determination process;

FIGS. 5A to 5C each represent a display example of a result of insurancesubscription propriety determination;

FIG. 6 represents an example of a screen for displaying a list of riskanalysis information of a plurality of subjects;

FIGS. 7A to 7F each represent an example of a detailed display of riskanalysis information of an individual subject; and

FIGS. 8A and 8B each represent a display example of future medicalexpenses.

DESCRIPTION OF THE EMBODIMENTS

Hereinafter, preferred embodiments of the present invention will bedescribed with reference to the drawings. However, it is to beunderstood that the description of the respective components presentedbelow are intended to be changed as deemed appropriate in accordancewith configurations and various conditions of apparatuses to which thepresent invention is to be applied and are not intended to limit thescope of the invention to the description presented below.

FIG. 1 schematically shows an overall configuration of a risk analysissystem according to an embodiment of the present invention.

A risk analysis system 1 is a system which, based on information relatedto the health of a subject (hereinafter, referred to as “subject data”),analyzes health risks (a hypertension risk, an event onset risk, and thelike) of the subject and generates output in accordance with a purposebased on an analysis result. The risk analysis system 1 according to thepresent embodiment is generally configured to include a database 10, ananalyzer 11, a medication support apparatus 12, and an output apparatus13.

The database 10 is a large-capacity storage apparatus which collects andmanages subject data. For example, subject data may include data onstatements of medical expenses, physical checkup data, vital data suchas blood pressure, medication information, and environmental data(information related to surrounding environment which may affect anevent onset risk such as structure of residence, climate in area ofresidence or place of work, and family makeup).

The analyzer 11 is a functional unit which analyzes health risks of asubject based on subject data. In addition, the medication supportapparatus 12 is a functional unit which determines a medication methodsuitable for a pathological condition of the subject based on subjectdata and a result of risk analysis. The analyzer 11 and the medicationsupport apparatus 12 preferably perform risk analysis and determine amedication method based on evidence (for example, guidelines provided bythe academic community, papers, and opinions of KOLs (Key OpinionLeaders)). Furthermore, the output apparatus 13 is a functional unitwhich generates a report based on an analysis result of the analyzer 11or a processing result of the medication support apparatus 12 andprovides the subject or a user of the system 1 with the report.Information provided by the output apparatus 13 is displayed on, forexample, a display apparatus of a terminal of the subject or a displayapparatus included in the system 1. As the terminal of the subject, forexample, a personal computer, a smartphone, a tablet terminal, or amobile phone can be used.

For example, the system 1 can be used by an individual subject for thepurposes of health management and self-check of risks or used by aphysician, a nurse, a public health nurse, or the like for the purposesof analyzing a health condition of a patient and creating a treatmentpolicy or guidance contents. In addition, the present system 1 can alsobe preferably applied to an insurance company for the purposes ofsubjectively analyzing health conditions and health risks of a client(an insurance subscription applicant) and using the analysis result topropose an insurance policy suitable for the client, determine apropriety of insurance subscription, set terms and conditions, and thelike.

The risk analysis system 1 can be constituted by a general-purposecomputer equipped with a CPU (a processor), a memory, a large-capacitystorage, an input apparatus (a keyboard, a mouse, a touch panel, or thelike), a display apparatus, a communication I/F, and the like. In thiscase, the risk analysis system 1 may be constituted by a single computeror the risk analysis system 1 may be realized by a plurality ofcomputers cooperating with one another. For example, constructing arobust and convenient system using techniques related to decentralizedcomputing or cloud computing is also desirable. When configuring thesystem using a plurality of computers, the computers may be installed atone location or installed in a distributed manner at a plurality oflocations (which may be in different countries). Each function of therisk analysis system 1 according to the present embodiment is realizedwhen a necessary program is executed by the CPU. However, a part of orall of the functions can also be constituted by a circuit such as anASIC or an FPGA.

(Analyzer)

FIG. 2 is a block diagram showing a functional configuration of theanalyzer 11. The analyzer 11 includes, as functions thereof, a dataacquiring unit 110, a hypertension determining unit 111, a riskestimating unit 112, a medical expense predicting unit 113, and aninsurance subscription propriety determining unit 114. The dataacquiring unit 110 is a function for acquiring necessary subject datafrom the database 10, and the hypertension determining unit 111 is afunction for determining hypertension based on the subject data. Therisk estimating unit 112 is a function for estimating an onset risk of acerebrovascular event or a coronary artery event based on a result ofthe hypertension determining unit 111 and subject data. The medicalexpense predicting unit 113 is a function for predicting medicalexpenses to be incurred in the future by the subject (referred to as“future medical expenses”) based on a result of the hypertensiondetermining unit 111 and a result of the risk estimating unit 112. Theinsurance subscription propriety determining unit 114 is a function fordetermining a propriety of subscription to health insurance by thesubject by comparing the predicted future medical expenses with aninsurance premium amount.

Hereinafter, a specific example of processing by the analyzer 11 will bedescribed.

(1) Acquisition of Subject Data

The data acquiring unit 110 acquires subject data necessary for riskanalysis from the database 10. In the present embodiment, at least bloodpressure data and physical checkup data of the subject are used assubject data.

As blood pressure data, desirably, both office blood pressure measuredat a hospital or the like and home blood pressure measured at home orthe like are acquired. In addition, as home blood pressure, three typesof data including morning blood pressure, evening blood pressure, andnight-time blood pressure are favorably acquired. Morning blood pressurerefers to blood pressure measured within one hour after awakening andmicturition and before taking breakfast, and evening blood pressurerefers to blood pressure measured before bedtime. Night-time bloodpressure refers to blood pressure measured while asleep. One to twoweeks' worth of data is favorably used for morning and evening bloodpressure, and one to two days' worth of data (with three to fourreadings taken per day) is favorably used for night-time blood pressure.

For example, in addition to basic data of the subject including age,gender, height, and weight, physical checkup data favorably includesinspection data such as a body-mass index (BMI), HDL cholesterol, LDLcholesterol, an estimated glomerular filtration rate (eGFR), fastingblood glucose, and HbA1c as well as reference data such as pre-existingdisorders, diseases currently being treated, medications currently beingtaken, and lifestyle habits (smoking habits, alcohol consumption,sleeping hours, and the like).

(2) Determination of Hypertension

The hypertension determining unit 111 determines hypertension of thesubject based on blood pressure data of the subject. Specifically, thehypertension determining unit 111 determines whether a blood pressurereading taken from the subject indicates normal range blood pressure orhypertension and, in the case of hypertension, determines a levelthereof. A determination procedure and determination criteria ofhypertension are desirably in accordance with reliable evidence. Thehypertension determining unit 111 according to the present embodimentdetermines a blood pressure level by a classification shown in Table 1in accordance with “Guidelines for the Management of Hypertension 2014(JSH 2014)” published by the Japanese Society of Hypertension. Althoughsystolic blood pressure is used for determining a blood pressure levelin the present embodiment, alternatively, diastolic blood pressure maybe used or both systolic blood pressure and diastolic blood pressure maybe used.

TABLE 1 Systolic blood pressure conditions Classification (Level) BP <140 mmHg Normal range BP BP = 140 mmHg~159 mmHg Grade I Hypertension BP= 160 mmHg~179 mmHg Grade II Hypertension BP ≥ 180 mmHg Grade IIIHypertension

In addition, when subject data includes data of both office bloodpressure and home blood pressure, the hypertension determining unit 111can determine a hypertension type in accordance with criteria shown inTable 2.

TABLE 2 Systolic blood pressure conditions Type of Hypertension OfficeBP ≥ 140 mmHg and Hypertension Home BP ≥ 135 mmHg Office BP < 140 mmHgand Masked Hypertension Home BP ≥ 135 mmHg Office BP ≥ 140 mmHg andWhite-coat Hypertension Home BP < 135 mmHg

White-coat hypertension refers to a state in which a blood pressurereading taken in a doctor's office exhibits hypertension butout-of-office blood pressure (such as home blood pressure) indicatesnormal range blood pressure. Conversely, masked hypertension refers to astate in which a blood pressure reading taken in a doctor's officeexhibits normal range blood pressure but out-of-office blood pressure(such as home blood pressure) indicates hypertension. Since maskedhypertension is manifested as three pathological conditions includingearly-morning hypertension, diurnal hypertension, and nocturnalhypertension, the hypertension determining unit 111 determines that apathological condition is any of early-morning, diurnal, and nocturnalhypertension based on a time point at which the home blood pressureexceeds 135 mmHg.

Furthermore, the hypertension determining unit 111 is also capable ofdetermining an abnormality in blood pressure based on a circadianvariability of blood pressure. For example, non-dipper nocturnalhypertension or riser nocturnal hypertension can be detected based on avariability of nocturnal blood pressure. Non-dipper nocturnalhypertension refers to a state where blood pressure does not drop duringnight-time (a normal person experiences a night-time dip in bloodpressure), and riser nocturnal hypertension refers to a state whereblood pressure rises during night-time.

(3) Estimation of Risk

The risk estimating unit 112 estimates an onset risk of acerebrovascular event or a coronary artery event based on subject data.In doing so, the risk estimating unit 112 may consider a determinationresult from the hypertension determining unit 111 as a risk factor. Thisis because hypertension is a factor of increasing the onset probabilityof a cerebrovascular event or a coronary artery event.

A risk estimation process by the risk estimating unit 112 is desirablyperformed in accordance with reliable evidence. In the presentembodiment, for example, the “Suita Score” developed by the NationalCerebral and Cardiovascular Center, the “Multipurpose Cohort Study (JPHCStudy)” by the National Cancer Center Japan, “NIPPON DATA 80/90”, or thelike is used.

The Suita Score is a risk score for predicting a 10-year onset risk of acoronary artery event (for example, ischemic heart diseases such asmyocardial infarction and angina). A 10-year onset risk of a coronaryartery event can be predicted by inputting, as risk factors, age,gender, smoking habits, presence of diabetes, hypertension level, LDLcholesterol, HDL cholesterol, and level of chronic kidney disease (CKD)(an eGFR value). In addition, a prediction model developed as an outcomeof the JPHC Study can be used to predict a 10-year onset risk of acerebrovascular event (for example, cerebral stroke or cerebralinfarction) by inputting, as risk factors, age, gender, smoking habits,a body-mass index (BMI), presence of diabetes, hypertension level, andpresence of oral administration of antihypertensive medication. Sincethese risk scores are well known, a detailed description will be omittedhere.

(4) Prediction of Future Medical Expenses

A specific flow of a prediction process of future medical expenses bythe medical expense predicting unit 113 according to the presentembodiment will now be described based on the flow chart shown in FIG. 3.

First, the medical expense predicting unit 113 acquires a hypertensionlevel (normal range/grade I hypertension/grade II hypertension/grade IIIhypertension) determined by the hypertension determining unit 111,respective onset probabilities of a coronary artery event and acerebrovascular event estimated by the risk estimating unit 112, andsubject data (steps S300 to S302). As subject data, at least informationon the gender and the age of a subject is used.

The medical expense predicting unit 113 respectively calculateshospitalization medical expenses necessary when developing a coronaryartery disease and hospitalization medical expenses necessary whendeveloping a cerebrovascular disease (step S303). Hospitalizationmedical expenses refer to total medical expenses necessary forhospitalization when acquiring a disease and may be calculated by thefollowing equation.Hospitalization medical expenses=one-time medical expenses+one-dayhospital stay medical expenses×length of stay

The “one-time medical expenses” refer to medical expenses for medicalactions that occur only once during a hospital stay. In the presentembodiment, a sum of medical expenses for three medical actionsincluding “first visit or revisit”, “surgery”, and “anesthesia” isadopted as the one-time medical expenses. The “one-day hospital staymedical expenses” refer to medical expenses necessary per day ofhospital stay. In the present embodiment, a sum of medical expenses perday for eight medical actions including “clinical administration, etc.”,“examination”, “diagnostic imaging”, “medication”, “injection”,“treatment”, “rehabilitation”, and “hospitalization charges, etc.” isadopted as the one-day hospital stay medical expenses. As an amount ofmedical expenses for each medical action, an average amount is favorablyobtained in advance based on statistical information. For example, inStatistics of Medical Care Activities in Public Health Insurancepublished by the Ministry of Health, Labour and Welfare, the number ofcases, the actual number of days for care, and the number of cases, thenumber of times, points, and the total number of medical treatment byeach medical action are compiled for each disease. Using suchstatistical information enables average medical expenses for eachmedical action to be calculated.

“Length of stay” is determined based on the gender and age of thesubject and the type of disease. In the present embodiment, Table 3(table for men) and Table 4 (table for women) are prepared in advance,and a length of stay corresponding to the gender and the age of thesubject and the type of disease is acquired. Average values based onstatistical information may be used as the values of the lengths of stayin the tables. For example, the following tables represent examplescreated based on the 2014 Patient Survey compiled by the Ministry ofHealth, Labour and Welfare.

TABLE 3 Coronary artery Cerebrovascular Age disease disease 20~24 6.421.4 25~29 6.5 23.2 30~34 4.4 33.6 35~39 5.8 52.5 40~44 6 38.1 45~49 5.937.6 50~54 5.8 68.5 55~59 5.4 47 60~64 5.8 52.9 65~69 5.7 64.1 70~74 5.759 75~79 7.6 70.9 80~84 9.3 77.6 85~89 12.9 97 90 and 12.7 132.3 older

TABLE 4 Coronary artery Cerebrovascular Age disease disease 20~24 2 23.825~29 3.3 62.9 30~34 8.4 21.1 35~39 6.1 43.2 40~44 4.9 30 45~49 6.4 26.550~54 5.2 30.9 55~59 4.6 37.6 60~64 4.8 48.9 65~69 10.4 70.3 70~74 5.876.3 75~79 6.9 88.6 80~84 11.2 101.6 85~89 21.6 153.1 90 and 57.7 214.6older

In addition, the medical expense predicting unit 113 respectivelycalculates annual outpatient medical expenses necessary for treating acoronary artery disease, annual outpatient medical expenses necessaryfor treating a cerebrovascular disease, and annual outpatient medicalexpenses for treating hypertension (step S304). Annual outpatientmedical expenses refer to total medical expenses necessary for hospitalvisits per year and may be calculated by the following equation.Annual outpatient medical expenses=one-time outpatient medicalexpenses×number of hospital visits per year

The “one-time outpatient medical expenses” refer to medical expensesnecessary per one hospital visit. In the present embodiment, a sum ofmedical expenses per day for nine medical actions including “first visitor revisit”, “clinical administration, etc.”, “examination”, “diagnosticimaging”, “medication”, “injection”, “treatment”, “rehabilitation”, and“hospitalization charges, etc.” is adopted as the one-time outpatientmedical expenses. As an amount of medical expenses for each medicalaction, an average amount is favorably obtained in advance based onstatistical information in a similar manner to hospitalization medicalexpenses. However, with respect to hypertensive diseases, inconsideration of the fact that dosage differs in accordance withhypertension level, medical expenses for “medication” are multiplied bya coefficient in accordance with the hypertension level.

The “number of hospital visits per year” is determined based on thegender and the age of the subject and the type of disease. Table 5 is atable defining average medical care intervals per disease and per agefor men, and FIG. 6 is a table defining average medical care intervalsper disease and per age for women. Using average medical care intervalsacquired from these tables, the number of hospital visits per year canbe obtained by the following equation.Number of hospital visits per year=365/average medical care intervals

An average value based on statistical information may be used as thevalue of the average medical care intervals. For example, the followingtables represent examples created based on the 2014 Patient Surveycompiled by the Ministry of Health, Labour and Welfare.

TABLE 5 Coronary Hypertensive artery Cerebrovascular Age disease diseasedisease 20~24 20.1 30 9.7 25~29 17.6 15.2 14.8 30~34 16.3 11 9.5 35~3918.3 14 12.1 40~44 19.8 16.4 9.6 45~49 20.6 15.4 9.3 50~54 21 16.7 12.755~59 21.3 16.5 14.1 60~64 20.8 17.7 13.4 65~69 20.5 18 14.1 70~74 1915.8 13.9 75~79 18 16.2 14.2 80~84 16.7 15.4 14.1 85~89 15.6 15.8 13.390 and 15.1 14.5 11.7 older

TABLE 6 Coronary Hypertensive artery Cerebrovascular Age disease diseasedisease 20~24 18.3 21 16.2 25~29 7.7 11.2 4 30~34 17.2 2.9 8.7 35~3914.8 13.7 5.9 40~44 19 9.4 11.8 45~49 18.2 17.8 13.4 50~54 20.3 14.812.4 55~59 20 16.7 12.9 60~64 20.2 16 12.6 65~69 19.7 16.7 15 70~74 1815.9 15.2 75~79 16.8 16.1 14.5 80~84 15.7 15.8 13.8 85~89 15.4 14.7 1290 and 14.8 13.6 12.5 older

Next, based on the hospitalization medical expenses calculated in stepS303 and the onset probability for each disease (step S305), the medicalexpense predicting unit 113 calculates future hospitalization medicalexpenses which are hospitalization medical expenses to be incurred inthe future by the subject using the following equation.Future hospitalization medical expenses=onset probability of coronaryartery disease×hospitalization medical expenses for coronary arterydisease+onset probability of cerebrovascular disease×hospitalizationmedical expenses for cerebrovascular disease

In addition, based on the annual outpatient medical expenses calculatedin step S304 and the onset probability for each disease (step S306), themedical expense predicting unit 113 calculates future outpatient medicalexpenses which are outpatient medical expenses to be incurred in thefuture by the subject using the following equation.Future outpatient medical expenses=onset probability of coronary arterydisease×annual outpatient medical expenses for coronary arterydisease+onset probability of cerebrovascular disease×annual outpatientmedical expenses for cerebrovascular disease+annual outpatient medicalexpenses for hypertensive disease

Next, the medical expense predicting unit 113 calculates a 10-year totalmedical expenses expectation value (step S307). The 10-year totalmedical expenses expectation value is a value representing how muchexpenses are to be required when it is assumed that an onset of adisease will inevitably occur once during a 10-year period. Assumingthat hospitalization accompanying the onset of the disease occurs onlyonce, the 10-year total medical expenses expectation value is calculatedby the following equation.10-year total medical expenses expectation value=future hospitalizationmedical expenses+(future outpatient medical expenses×10)/2

According to the processes described above, future hospitalizationmedical expenses, future outpatient medical expenses, and a 10-yeartotal medical expenses expectation value can be calculated.

(5) Propriety Determination of Insurance Subscription

A specific flow of an insurance subscription propriety determinationprocess by the insurance subscription propriety determining unit 114according to the present embodiment will now be described based on theflow chart shown in FIG. 4 .

First, the insurance subscription propriety determining unit 114 setsterms and conditions (an insurance plan, a coverage amount, add-onoptions, a policy duration, and the like) of health insurance to beconsidered (step S400). The terms and conditions may be input orselected by the subject or the insurance subscription proprietydetermining unit 114 may automatically set recommended conditions inaccordance with the age, the gender, and preferences of the subject.

Next, based on the set terms and conditions and information such as theage and the gender of the subject, the insurance subscription proprietydetermining unit 114 estimates an insurance premium amount to be paid bythe subject when subscribing to the health insurance (step S401). Theinsurance premium amount may be calculated on a single-year basis or anamount over the duration of the insurance policy may be calculated. Inthe present embodiment, it is assumed that a total insurance premiumamount to be paid over 10 years is calculated for the purpose ofcomparing with future medical expenses.

Next, the insurance subscription propriety determining unit 114determines a propriety of the subject subscribing to health insurance bycomparing the insurance premium amount estimated in step S401 with thefuture medical expenses (for example, the 10-year total medical expensesexpectation value) predicted by the medical expense predicting unit 113(step S402), and outputs a determination result (step S403).

A logic of propriety determination differs depending on who uses thepresent system 1 for what kind of purpose. Specifically, when thesubject (an insurance subscription applicant) uses the present system 1for the purpose of considering health insurance that is suitable for thesubject, since the benefit of the subject should be prioritized, adetermination result of “subscription: appropriate” is favorably outputwhen the insurance premium amount is smaller than the 10-year totalmedical expenses expectation value and a determination result of“subscription: inappropriate” is favorably output when the insurancepremium amount is equal to or larger than the 10-year total medicalexpenses expectation value. FIG. 5A represents a display example of adetermination result to be presented to an insurance subscriptionapplicant.

On the other hand, when an insurer who provides health insurance usesthe present system 1 for the purpose of underwriting a subject (aninsurance subscription applicant), since the benefit of the insurershould be prioritized, a determination result of “subscription:appropriate” is favorably output when the insurance premium amount islarger than the 10-year total medical expenses expectation value and adetermination result of “subscription: inappropriate” is favorablyoutput when the insurance premium amount is equal to or smaller than the10-year total medical expenses expectation value. FIG. 5B represents adisplay example of a determination result of a propriety of underwritingto be presented to an insurer.

The determination logic and display examples described herein are merelyexemplary and other logic or criteria may be used. It is also preferableto output conditions and an insurance premium amount of health insurancesuitable for the subject (the insurance subscription applicant) based onthe determination result from the insurance subscription proprietydetermining unit 114. For example, optimal terms and conditions whichprovide a balance between the insurance premium amount and the 10-yeartotal medical expenses expectation value may be selected from aplurality of terms and conditions and recommended to the subject (theinsurance subscription applicant) or to the insurer. FIG. 5C representsan example of a recommendation output by the output apparatus 13.Alternatively, instead of a two-alternative choice between appropriateand inappropriate, a determination result may be output in a pluralityof stages such as most appropriate, appropriate, and inappropriate.Performing such an output is advantageous for a person consideringsubscribing to insurance because the person can more readily select aninsurance policy with suitable conditions and is also advantageous foran insurer because an increase in opportunities to subscribe to healthinsurance can be expected.

(Display Example of Risk Analysis Result)

A display example of a risk analysis result by the output apparatus 13will be described.

FIG. 6 represents an example of a screen for displaying a list of riskanalysis information of a plurality of subjects. Each row representsrisk analysis information of one subject. In the example shown in FIG. 6, information related to a subject such as a user ID, an age, a gender,a trend in blood pressure, office blood pressure, home blood pressure, avascular age, and a total risk is output as risk analysis information.As the trend in blood pressure, for example, a weekly variation (agraph) in systolic blood pressure or an average value, a maximum value,a minimum value, or the like of systolic blood pressure in a week may bedisplayed. The total risk is an index representing a comprehensivedetermination of onset risks of a cerebrovascular event and a coronaryartery event and, in the example shown in FIG. 6 , the total risk isindicated in four stages including “Low”, “Middle”, “High”, and “VeryHigh”. In addition, an alert icon is displayed for subjects whose totalrisk is determined to be “Very High”.

FIGS. 7A to 7F each represent an example of a detailed display of riskanalysis information of an individual subject. FIG. 7A shows a displayexample of a stratification of blood pressure. An abscissa representstime of day (from 0 to 24 hours), and an ordinate represents bloodpressure readings. Data plotted by triangles represent blood pressure(morning and evening blood pressure) readings taken during daytime anddata plotted by squares represent blood pressure (nocturnal bloodpressure) readings taken during nighttime. In the graph shown in FIG.7A, respective blood pressure ranges of normal blood pressure, grade Ihypertension, grade II hypertension, and grade III hypertension aredepicted in different colors and, accordingly, in which blood pressurerange a blood pressure reading of the subject is classified can bereadily confirmed. In FIG. 7A, a systolic blood pressure range of 140 to159 is adopted as grade I hypertension, a systolic blood pressure rangeof 160 to 179 is adopted as grade II hypertension, and a systolic bloodpressure range of 180 and higher is adopted as grade III hypertension.

In addition, when white-coat hypertension, masked hypertension, anabnormal circadian variability of blood pressure (such as non-dippernocturnal hypertension or riser nocturnal hypertension), and the likeare detected in blood pressure data of the subject, such information mayalso be output.

FIG. 7B shows another display example of a stratification of bloodpressure. An abscissa represents office blood pressure and an ordinaterepresents home blood pressure. The graph shown in FIG. 7B similarlydepicts respective blood pressure ranges of normal blood pressure, gradeI hypertension, grade II hypertension, and grade III hypertension indifferent colors. Data plotted by a circle indicates statistical valuesof blood pressure readings taken during a prescribed period of time.Specifically, a center of the circle represents an average value ofblood pressure and a size (a diameter) of the circle represents avariation (a standard deviation, dispersibility, or the like) in bloodpressure readings. Such a display enables in which blood pressure rangea blood pressure reading of the subject is classified to be readilyconfirmed by looking at a position of the circle. In addition, when aclassification according to office blood pressure (the abscissa) and aclassification according to home blood pressure (the ordinate) areinconsistent, white-coat hypertension or masked hypertension issuspected. Furthermore, looking at the size of the circle enableswhether blood pressure readings are stable or vary significantly to beintuitively assessed.

FIG. 7C represents a display example of a cerebrovascular risk. Acomparison between the subject's score and an average score for thesubject's age group is shown for each of five risk factors includingblood pressure, body-mass index (BMI), smoking habits, diabetes, andblood pressure medication. In addition, FIG. 7D represents a displayexample of a coronary artery risk. A comparison between the subject'sscore and an average score for the subject's age group is shown for eachof six risk factors including blood pressure, chronic kidney disease (anestimated glomerular filtration rate eGFR), smoking habits, diabetes,HDL cholesterol, and LDL cholesterol. By viewing the charts provided inFIGS. 7C and 7D, a presence or absence of a cerebrovascular risk and acoronary artery risk (whether or not there is a deviation from theaverage), items to be improved, and the like can be readilycomprehended.

FIG. 7E represents a display example of respective onset risks of acerebrovascular event and a coronary artery event. An ordinaterepresents a probability of developing an event within 10 years. Bycomparing and displaying an event onset risk of the subject and anaverage event onset risk for the subject's age group, how high or lowthe event onset risk of the subject is can be readily accessed. Inaddition, FIG. 7F represents a display example of a total risk whichcombines an onset risk of a cerebrovascular event and an onset risk of acoronary artery event. For example, a score obtained by weighting andadding up the onset risk of a cerebrovascular event and the onset riskof a coronary artery event and normalizing the sum within a range of 0(minimum risk) to 100 (maximum risk) can be used as the total risk.

(Display Example of Future Medical Expenses)

FIGS. 8A and 8B each show a display example of future medical expensesby the output apparatus 13. FIG. 8A represents a screen example forpresenting a 10-year total medical expenses expectation value predictedby the medical expense predicting unit 113 to the subject.

FIG. 8B represents a screen example which shows predicted values ofannual future medical expenses as a graph. For example, estimation ofmedical expenses per year can be performed as follows.

When a 10-year onset probability of a given disease as estimated by therisk estimating unit 112 is denoted by p {p|0≤p≤1} and a year to becalculated is denoted by y{y|1≤y≤10}, a disease incidence P(y) in eachyear y can be calculated as follows. It should be noted that while asimple model for linearly predicting a disease onset probability is usedin this case, a nonlinear model may be used instead.

$\begin{matrix}{{P(y)} = \frac{py}{10}} & \left\lbrack {{Math}.\mspace{14mu} 1} \right\rbrack\end{matrix}$

When additional medical expenses to be incurred when developing adisease is denoted by Cp [yen] and basic annual medical expenses to beincurred independently of the disease is denoted by Cn [yen/year],average medical expenses μ(y) and a dispersion of medical expenses σ(y)in each year y are respectively calculated as follows.μ(y)=(Cp+Cn)P(y)+Cn{1−P(y)}σ(y)=√{square root over ({(Cp+Cn−μ(y)}²P(y)+{Cn−μ(y)}²{1−P(y)})}  [Math. 2]

In this case, the additional medical expenses Cp is a sum ofhospitalization medical expenses and outpatient medical expensescalculated by the medical expense predicting unit 113.

A graph A in FIG. 8B represents a trend in the average medical expensesμ(y) in a case where an onset probability p of a disease=0.1, additionalmedical expenses Cp=10,000,000 [yen], and basic annual medical expensesCn=10,000 [yen/year]. In addition, graphs B, C, and D are graphsindicating a more pessimistic prediction than graph A, in which graphsB, C, and D respectively represent μ(y)+σ(y), μ(y)+2σ(y), andμ(y)+3σ(y). Viewing such graphs enable a prediction of medical expensesand a range of variation (a probability distribution) of predictedvalues for each year to be assessed. For example, the graph may be usedby a subject as reference for a financial plan when consideringsubscription to health insurance or formulating a savings plan or may beused as reference information when an insurer who provides healthinsurance evaluates underwriting or designs terms and conditions for asubject (an insurance subscription applicant).

Advantages of the Present Embodiment

According to the configuration of the present embodiment describedabove, an event onset risk of a subject is first estimated and futuremedical expenses of the subject are predicted based on the event onsetrisk. Therefore, future medical expenses which take health conditionsand health risks of the subject into consideration can be predicted.Furthermore, since risk estimation and medical expense prediction areperformed based on reliable evidence and statistical data of publicorganizations (such as the Ministry of Health, Labour and Welfare), highreliability and accuracy are expected.

In addition, the present embodiment adopts an algorithm which predictsfuture medical expenses based on medical expenses needed when a diseasedevelops and an onset probability of the disease. Since “medicalexpenses needed when a disease develops” is not dependent on anindividual, an average amount of medical expenses can be easily obtainedfrom, for example, previous data on statements of medical expenses,statistical data compiled by the Ministry of Health, Labour and Welfare,and the like. In addition, by combining the average medical expenseswith the “onset probability” which is individual-dependent, futuremedical expenses which takes health conditions and health risks of thesubject into consideration can be calculated.

In addition, in the present embodiment, since both hospitalizationmedical expenses and outpatient medical expenses are taken intoconsideration, more realistic medical expenses can be predicted.Furthermore, in the present embodiment, since risk estimation andprediction of future medical expenses are performed for two types ofdiseases, namely, a coronary artery event and a cerebrovascular event,the accuracy of predicting medical expenses can be increased. Moreover,in the present embodiment, since future medical expenses of a subject ispredicted by also taking medical expenses needed to treat hypertensioninto consideration, the prediction accuracy of future medical expensescan be further increased.

In addition, since the present embodiment provides a function forautomatically determining a propriety of insurance subscription based onfuture medical expenses and an insurance premium amount, an insurancesubscription applicant or an insurer can readily determine the proprietyof subscription to health insurance with high reliability.

Other Embodiments

The configuration of the embodiment described above merely represents aspecific example of the present invention. The scope of the presentinvention is not limited to the embodiment described above and variousmodifications can be made without departing from the scope of technicalconcepts of the invention.

For example, while evidence based on results of research performed inJapan such as the Suita Score and the JPHC Study are used in theembodiment described above, when the subject of the present system 1 isnon-Japanese or resides in a country other than Japan, evidence inaccordance with the subject's race or area of residence is desirablyused. This is because different races carry different event onset risksdue to differences in genetics, and even within the same race, adifference in areas of residence results in a difference in event onsetrisks due to differences in lifestyle habits. Furthermore, sincedifferent areas of residence have different healthcare systems andcommodity prices which result in different medical expenses, thealgorithm for predicting medical expenses may also be changed for eacharea of residence of the subject.

Specifically, a plurality of types of analysis algorithms (programs,tables, or the like) for different races and/or different areas ofresidence may be provided in advance and the risk estimating unit 112 orthe medical expense predicting unit 113 may select an analysis algorithmin accordance with a race and/or area of residence of the subject.Information regarding the race and the area of residence can be suppliedas subject data. In this manner, providing a function for adaptivelychanging algorithms in accordance with the race and the area ofresidence of the subject is advantageous in operating the system incountries made up of people of various races such as the United States.

What is claimed is:
 1. A risk analysis system, comprising: a storageapparatus which stores subject data including information related tohealth of a subject; an analyzer which analyzes a risk related to thehealth of the subject based on the subject data acquired from thestorage apparatus; and an output apparatus which outputs an analysisresult by the analyzer, wherein the analyzer includes: a risk estimatingunit which estimates an event onset risk of the subject based on thesubject data; a medical expense predicting unit which predicts futuremedical expenses, which are medical expenses to be incurred in thefuture by the subject, based on the event onset risk estimated by therisk estimating unit and the subject data; and a calculating unit whichestimates, based on a set condition of health insurance, an insurancepremium amount to be paid in a case where the subject subscribes to thehealth insurance, the medical expense predicting unit predicts thepredicted future medical expenses according to the following equations(1) to (3): $\begin{matrix}{{P(y)} = \frac{py}{10}} & (1) \\{{\mu(y)} = {{\left( {{Cp} + {Cn}} \right){P(y)}} + {{Cn}\left\{ {1 - {P(y)}} \right\}}}} & (2) \\{{{\sigma(y)} = \sqrt{\left\{ {{\left( {{Cp} + {Cn} - {\mu(y)}} \right\}^{2}{P(y)}} + {\left\{ {{Cn} - {\mu(y)}} \right\}^{2}\left\{ {1 - {P(y)}} \right\}}} \right.}},} & (3)\end{matrix}$ where a 10-year onset probability of a given disease asestimated by the risk estimating unit is p {p|0≤p≤1}, a year to becalculated is y {y|1≤y≤10}, a disease incidence in each year y is P(y),additional medical expenses incurred in a case of developing a diseaseis Cp, basic annual medical expenses incurred independently of thedisease is Cn, average medical expenses is μ(y), and a dispersion ofmedical expenses is σ(y), and the output apparatus presents thecondition of the health insurance, the estimated insurance premiumamount, and the predicted future medical expenses to the subject.
 2. Therisk analysis system according to claim 1, wherein the medical expensepredicting unit calculates the additional medical expenses Cp based onhospitalization medical expenses required for hospitalization andoutpatient medical expenses required for outpatient care.
 3. The riskanalysis system according to claim 1, wherein the risk estimating unitestimates the onset probability for each of a plurality of diseases, andthe medical expense predicting unit predicts the future medical expensesfor each of the plurality of diseases, and calculates total futuremedical expenses of the subject by adding up the predicted futuremedical expenses.
 4. The risk analysis system according to claim 1,further comprising an insurance subscription propriety determining unitwhich, by comparing the predicted future medical expenses with theestimated insurance premium amount, determines a propriety ofsubscription to the health insurance by the subject.
 5. The riskanalysis system according to claim 4, wherein the output apparatuspresents conditions and/or an insurance premium amount of healthinsurance suitable for the subject based on a determination result fromthe insurance subscription propriety determining unit.
 6. The riskanalysis system according to claim 4, wherein the insurance subscriptionpropriety determining unit determines that it is appropriate for thesubject to subscribe to the health insurance in a case where theestimated insurance premium amount is smaller than the predicted futuremedical expenses.
 7. The risk analysis system according to claim 1,wherein the condition of health insurance is input or selected by thesubject.
 8. The risk analysis system according to claim 1, wherein theoutput apparatus generates and outputs a graph indicating an annualtrend of the future medical expenses of the subject.
 9. The riskanalysis system according to claim 1, wherein the analyzer includes aplurality of types of analysis algorithms for different races and/ordifferent areas of residence and selects an analysis algorithm inaccordance with a race and/or area of residence of the subject.
 10. Therisk analysis system according to claim 1, wherein the analyzer furtherincludes a hypertension determining unit which determines hypertensionof the subject based on the subject data, and the risk estimating unitestimates the event onset risk of the subject using a determinationresult from the hypertension determining unit as a risk factor.
 11. Therisk analysis system according to claim 10, wherein the subject dataincludes blood pressure data of the subject for a plurality of timeswhich are measured during a predetermined period of time, thehypertension determining unit calculates a statistical value of bloodpressure of the subject from the blood pressure data for the pluralityof times to determine a level of hypertension of the subject based onthe statistical value of blood pressure, and the risk estimating unitestimates the event onset risk of the subject based on the subject dataand the level of hypertension.
 12. The risk analysis system according toclaim 11, wherein, in a case where it is determined by the hypertensiondetermining unit that the subject has hypertension, the medical expensepredicting unit predicts the future medical expenses of the subject byalso taking medical expenses needed to treat hypertension intoconsideration.
 13. A risk analysis method, comprising: an acquiring stepof acquiring subject data including information related to health of asubject; an analyzing step of analyzing a risk related to the health ofthe subject based on the subject data; and an outputting step ofoutputting an analysis result of the analyzing step, wherein theanalyzing step includes: a step of estimating an event onset risk of thesubject based on the subject data; a step of predicting future medicalexpenses, which are medical expenses to be incurred in the future by thesubject, based on the estimated event onset risk and the subject data;and a step of estimating, based on a set condition of health insurance,an insurance premium amount to be paid in a case where the subjectsubscribes to the health insurance, the medical expense predicting unitpredicts the predicted future medical expenses according to thefollowing equations (1) to (3): $\begin{matrix}{{P(y)} = \frac{py}{10}} & (1) \\{{\mu(y)} = {{\left( {{Cp} + {Cn}} \right){P(y)}} + {{Cn}\left\{ {1 - {P(y)}} \right\}}}} & (2) \\{{{\sigma(y)} = \sqrt{\left\{ {{\left( {{Cp} + {Cn} - {\mu(y)}} \right\}^{2}{P(y)}} + {\left\{ {{Cn} - {\mu(y)}} \right\}^{2}\left\{ {1 - {P(y)}} \right\}}} \right.}},} & (3)\end{matrix}$ where a 10-year onset probability of a given disease asestimated by the risk estimating unit is p {p|0≤p≤1}, a year to becalculated is y {y|1≤y≤10}, a disease incidence in each year y is P(y),additional medical expenses incurred in a case of developing a diseaseis Cp, basic annual medical expenses incurred independently of thedisease is Cn, average medical expenses is μ(y), and a dispersion ofmedical expenses is σ(y), and the outputting step includes a step ofpresenting the condition of the health insurance, the estimatedinsurance premium amount, and the predicted future medical expenses tothe subject.
 14. A non-transitory computer readable medium that stores aprogram causing a computer to execute a risk analysis method, the riskanalysis method comprising: an acquiring step of acquiring subject dataincluding information related to health of a subject; an analyzing stepof analyzing a risk related to the health of the subject based on thesubject data; and an outputting step of outputting an analysis result ofthe analyzing step, wherein the analyzing step includes: a step ofestimating an event onset risk of the subject based on the subject data;a step of predicting future medical expenses, which are medical expensesto be incurred in the future by the subject, based on the estimatedevent onset risk and the subject data; and a step of estimating, basedon a set condition of health insurance, an insurance premium amount tobe paid in a case where the subject subscribes to the health insurance,the medical expense predicting unit predicts the predicted futuremedical expenses according to the following equations (1) to (3):$\begin{matrix}{{P(y)} = \frac{py}{10}} & (1) \\{{\mu(y)} = {{\left( {{Cp} + {Cn}} \right){P(y)}} + {{Cn}\left\{ {1 - {P(y)}} \right\}}}} & (2) \\{{{\sigma(y)} = \sqrt{\left\{ {{\left( {{Cp} + {Cn} - {\mu(y)}} \right\}^{2}{P(y)}} + {\left\{ {{Cn} - {\mu(y)}} \right\}^{2}\left\{ {1 - {P(y)}} \right\}}} \right.}},} & (3)\end{matrix}$ where a 10-year onset probability of a given disease asestimated by the risk estimating unit is p {p|0≤p≤1}, a year to becalculated is y {y|1≤y≤10}, a disease incidence in each year y is P(y),additional medical expenses incurred in a case of developing a diseaseis Cp, basic annual medical expenses incurred independently of thedisease is Cn, average medical expenses is μ(y), and a dispersion ofmedical expenses is σ(y), and the outputting step includes a step ofpresenting the condition of the health insurance, the estimatedinsurance premium amount, and the predicted future medical expenses tothe subject.
 15. A display apparatus which displays the analysis resultoutput by a risk analysis method, the risk analysis method comprising:an acquiring step of acquiring subject data including informationrelated to health of a subject; an analyzing step of analyzing a riskrelated to the health of the subject based on the subject data; and anoutputting step of outputting an analysis result of the analyzing step,wherein the analyzing step includes: a step of estimating an event onsetrisk of the subject based on the subject data; a step of predictingfuture medical expenses, which are medical expenses to be incurred inthe future by the subject, based on the estimated event onset risk andthe subject data; and a step of estimating, based on a set condition ofhealth insurance, an insurance premium amount to be paid in a case wherethe subject subscribes to the health insurance, the medical expensepredicting unit predicts the predicted future medical expenses accordingto the following equations (1) to (3): $\begin{matrix}{{P(y)} = \frac{py}{10}} & (1) \\{{\mu(y)} = {{\left( {{Cp} + {Cn}} \right){P(y)}} + {{Cn}\left\{ {1 - {P(y)}} \right\}}}} & (2) \\{{{\sigma(y)} = \sqrt{\left\{ {{\left( {{Cp} + {Cn} - {\mu(y)}} \right\}^{2}{P(y)}} + {\left\{ {{Cn} - {\mu(y)}} \right\}^{2}\left\{ {1 - {P(y)}} \right\}}} \right.}},} & (3)\end{matrix}$ where a 10-year onset probability of a given disease asestimated by the risk estimating unit is p {p|0≤p≤1}, a year to becalculated is y {y|1≤y≤10}, a disease incidence in each year y is P(y),additional medical expenses incurred in a case of developing a diseaseis Cp, basic annual medical expenses incurred independently of thedisease is Cn, average medical expenses is μ(y), and a dispersion ofmedical expenses is σ(y), and the outputting step includes a step ofpresenting the condition of the health insurance, the estimatedinsurance premium amount, and the predicted future medical expenses tothe subject.